The present invention relates to sharing of information by mobile communication devices having users within a social mobile network.
As information technology proliferates, the demand for effective and efficient retrieval of information continues to rise. To address this demand, a variety of search solutions have been developed to simplify the process of information retrieval and/or to improve the relevance of the information that is retrieved. Today, information retrieval is possible from virtually any computing device. Despite advances in search-related technology, existing search solutions in the mobile space fail to address characteristics unique to such devices and to the people who use them.
Search solutions enable end-users as well as software programs to search for information in various sources including standalone databases, databases integrated with a specific software application, and content servers containing a collection of documents and files. Search solutions exist for almost every computing device including mainframes, mini-computers, PCs and mobile devices. Query languages, query tools and even artificial intelligence systems have been developed to simplify the retrieval process. Various techniques have been employed to improve the relevance of the results that are returned. The widespread deployment and use of computer networks, including the Internet, has led to search solutions, e.g., an internet search service, where keywords are entered into a browser and the resulting query is performed to compile information that is aggregated from multiple sources.
Conventional solutions typically enable information retrieval from one of two types of information sources, namely those containing structured data, and those containing unstructured data. Structured data usually refers to information stored in databases. Search solutions for structured data include (i) query languages like SQL, (ii) reporting tools that simplify or eliminate the need to use SQL, and (iii) database-specific search modules integrated as features within software applications that manage those databases, such as those enabling search for names within a phone book. Unstructured data usually refers to text documents. Search solutions for unstructured data include (i) search for documents and files by name, such as that provided by file explorers, (ii) keyword search for words within the documents or the associated meta-data, such as enterprise and desktop search solutions, and (iii) search for content aggregated from the world-wide web, such as that provided by internet search engines.
Device search refers to search for information on a specific computing device using a search application or tool running on the device. Accordingly, the retrievable information is limited to the data which is located on the device or on a medium attached to it, such as an external drive. The process of searching for data on a device is typically determined by the end-user's knowledge of the information that he/she seeks. For example, if the end-user desires to listen to a song titled “Jimmy Jazz”, stored on a device containing thousands of music files, the end-user may utilize a search feature within a music player software application to retrieve all songs that include the words “jimmy” and “jazz” in their song title.
Internet search refers to searching for information that exists on the World Wide Web and the internet in general. Typically, the end-user uses an Internet browser running on a computing device to access an Internet search engine. The search engine typically maintains its own information source which includes content aggregated from the World Wide Web. If the end-user searches, for example, for “jimmy jazz”, an Internet search may return millions of hits, whereas a device search may typically return only a handful of results.
In general, the usefulness of an information retrieval system depends on the relevance of the results that it finds. Many of the presently available information retrieval systems rank their results using a relevance measurement metric. In accordance with this scheme, the greater the relevance of a result, the higher is its position in the compiled list. In the case of an Internet search, there may be billions of web pages or documents that match a particular keyword query. Internet search solutions may deem a page to be more relevant based on, for example, the popularity of the web page, an authoritative factor assigned to the content source, frequency of the keywords within the document, or the like. Similarly, device search solutions may calculate a relevance measurement by assigning more importance to keyword matches within certain fields of data records. For example, a search for “Jimmy Jazz” in the device address book may rank records where the first name field or last name field contains “Jimmy” or “Jazz” higher than records where the company name field or job title field contains “Jimmy” or “Jazz”.
Mobile network operators, device manufacturers, operating system providers and independent companies operate online stores where users of mobile devices can purchase applications and other content, such as music or videos. Such content providers face challenges in getting users to search through the often vast inventory of available applications and assess the value of those applications.